卷积神经网络是近年发展起来，并引起广泛重视的一种高效识别方法。20世纪60年代，Hubel和Wiesel在研究猫脑皮层中用于局部敏感和方向选择的神经元时发现其独特的网络结构可以有效地降低反馈神经网络的复杂性，继而提出了卷积神经网络。

资源内容为卷积神经网络实例（手写字母识别）--PyTorch。 如果没有积分或者打不开的朋友可以联系邮箱：1454196320@qq.com

【达摩老生出品，必属精品，亲测校正，质量保证】 资源名：卷积神经网络CNN进行图像分类_matlab 资源类型：matlab项目全套源码 源码说明： 全部项目源码都是经过测试校正后百分百成功运行的，如果您下载后不能运行可联系我进行指导或者更换。 适合人群：新手及有一定经验的开发人员

mdCNN is a Matlab framework for Convolutional Neural Network (CNN) supporting 1D, 2D and 3D kernels. Network is Multidimensional, kernels are in 3D and convolution is done in 3D. It is suitable for volumetric input such as CT / MRI / video sections. But can also process 1d/2d images. Framework supports all the major features such as dropout, padding, stride, max pooling, L2 regularization, momentum, cross entropy, MSE. The framework Its completely written in Matlab, No dependencies are needed. It is pretty optimized, when training or testing all of the CPU cores are participating using Matlab Built-in Multi-threading. There are several examples for training a network on MNIST, CIFAR10, 1D CNN, and MNIST3d - a special expansion of MNIST dataset to 3D volumes. MNIST Demo will download the dataset and start the training process. It will reach 99.2% in several minutes. CIFAR10 demo reaches about 80% but it takes longer to converge. For 3D volumes there is a demo file that will creates a 3d volume from each digit in MNIST dataset, then starts training on the 28x28x28 samples. It will reach similar accuracy as in the 2d demo This framework was used in a project classifying Vertebra in a 3D CT images. =~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~ To run MNIST demo: Go into the folder 'Demo/MNIST' , Run 'demoMnist.m' file. After 15 iterations it will open a GUI where you can test the network performance. In addition layer 1 filters will be shown. To run MNIST3D demo: Go into the folder 'Demo/MNIST3d' , and run 'demoMnist3D.m' file. =~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~ Check the 'mdCNN documentation.docx' file for more specification on how to configure a network For general questions regarding network design and training, please use this forum https://groups.google.com/forum/#!forum/mdcnn-multidimensional-cnn-library-in-matlab Any other issues you can contact me at hagaygarty@gmail.com Please use matlab 2014 and above

基于CNN卷积神经网络识别mnist手写数据集所有源码，包括误差反向传播实现的各种层以及加载mnist数据集的方法

这个主要是CNN的推导和实现的一些笔记，再看懂这个笔记之前，最好具有CNN的一些基础。

带教程完整的cnn 运行源码，最简单的CNN

在matlab中使用deepLearnToolbox实现卷积神经网络的简单教程

1、Mnist_cnn.py 该脚本文件 用TensorFlow框架 实现CNN卷积神经网络处理Mnist手写数字识别数据集，准确率达到99.21%； 2、Mnist_cnn_tensorboard.py 该脚本文件在Mnist_cnn.py的基础上实现可视化。

本人用CNN卷积神经网络编写的手写字体识别，大家可以下载下来参考下，研究生课程的作业